[time]
2020-11-20T04:50:07+01:00

[team_name]
WiMap

[team_institution]
Beijing University of Posts and Telecommunications 

[logolink]


[system_name]


[website]


[track]
3

[reference_person]
Chen, Runze

[email]
chenrz925@bupt.edu.cn

[description]
We provide complementary registration information by sending a email with a supplementary word document to the competition chairs.

Smartphones have become an indispensable tool in the work and life of most people. Given the many kinds of sensors built into smartphones, solving indoor navigation problems using these phones has become feasible. This paper proposes such a solution, using a neural network-based indoor positioning method. This method improves the positioning accuracy and helps improve the user experience. The major features of the method are that it leverages and fuses the advantages of two different indoor positioning methods [pedestrian dead reckoning (PDR) and neural network-based Wi-Fi indoor positioning], and using a particle filter to considers the influence on the positioning of the indoor spatial map. In the training process, the smartphone built-in sensors are used as data sources to provide fine-grained position observations. The sparse Wi-Fi signal is trained for regressing a position in which the label is provided by PDR mothed. In the test process, Wi-Fi signals are used as observations in the particle filter. 

[references]